Application of Reinforcement Learning to Multi-Agent Production Scheduling

Reinforcement learning (RL) has received attention in recent years from agent-based researchers because it can be applied to problems where autonomous agents learn to select proper actions for achieving their goals based on interactions with their environment. Each time an agent performs an action, the environment¡Šs response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent¡Šs goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. The objective of this research is to develop a set of guidelines for applying the Q-learning algorithm to enable an individual agent to develop a decision making policy for use in agent-based production scheduling applications such as dispatching rule selection and job routing. For the dispatching rule selection problem, a single machine agent employs the Q-learning algorithm to develop a decision-making policy on selecting the appropriate dispatching rule from among three given dispatching rules. In the job routing problem, a simulated job shop system is used for examining the implementation of the Q-learning algorithm for use by job agents when making routing decisions in such an environment. Two factorial experiment designs for studying the settings used to apply Q-learning to the single machine dispatching rule selection problem and the job routing problem are carried out. This study not only investigates the main effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling

Artefactual literacy-related abilities for historians’ effective seeking and use of primary resources

This study explored historians’ archival search behaviour from Yakel and Torres’ User Expertise in Archives (UEA) model. The model contained three types of knowledge that influence archival researchers’ information seeking: domain knowledge, artefactual literacy, and archival intelligence. This paper reported on the artefactual literacy-related abilities and behaviour. A naturalistic inquiry approach was adopted to study twelve history master’s students’ thesis research behaviour. In-depth interviewing was used to collect narratives of archival search experiences. The inductive constant comparisons and open/axial coding were used in the analysis. Artefactual literacy related abilities and behaviour were categorized into external criticism-related and internal criticism-related. The former included three abilities: understanding the production context of primary sources, the ability to differentiate between intentional and unintentional sources, and the ability to cope with language limitations. The latter included two abilities: putting oneself in the shoe of the historical figure understudy, and comparing documents with different perspectives. UEA is a powerful analytic model for studying archive users’ information behaviour. Archivists should care about how domain knowledge and artefactual literacy influenced archival search decisions and actions.Peer Reviewe

[2,9-Bis(3,5-dimethyl-1H-pyrazol-1-yl-κN 2)-1,10-phenanthroline-κ2 N,N′]bis­(thio­cyanato-κN)cadmium(II)

In the title complex, [Cd(NCS)2(C22H20N6)], the CdII ion is in a CdN6 coordination geometry which is inter­mediate between octa­hedral and trigonal–prismatic. The dihedral angles formed between the mean planes of the pyrazole rings and the phenanthroline system are 15.74 (15) and 16.30 (13)°. In the crystal, there is a π–π stacking inter­action involving two symmetry-related pyrazole rings, with a centroid–centroid distance of 3.664 (3) Å. In addition, there is a relatively short inter­molecular contact between C atoms [C⋯C = 3.399 (6) Å] involving symmetry-related pyridine rings along the a axis

Heterogenous scaling in the inter-event time of on-line bookmarking

In this paper, we study the statistical properties of bookmarking behaviors in Delicious.com. We find that the inter-event time (τ) distributions of bookmarking decay in a power-like manner as τ increases at both individual and population levels. Remarkably, we observe a significant change in the exponent when the inter-event time increases from the intra-day range to the inter-day range. In addition, the dependence of the exponent on individual activity is found to be different in the two ranges. Instead of monotonically increasing with activity, the inter-day exponent peaks around 3. These results suggest that the mechanisms driving human actions are different in the intra-day and inter-day ranges. We further show that the global distributions of less active users are closer to an exponential distribution than those of more active users. Moreover, a universal behavior in the inter-day range is observed by considering the rescaled variable τ/left angle bracketτright-pointing angle bracket. Finally, the possible causes of these phenomena are discussed

Quantum theory of light diffraction

At present, the theory of light diffraction only has the simple wave-optical approach. In this paper, we study light diffraction with the approach of relativistic quantum theory. We find that the slit length, slit width, slit thickness and wave-length of light have affected to the diffraction intensity and form of diffraction pattern. However, the effect of slit thickness on the diffraction pattern can not be explained by wave-optical approach, and it can be explained in quantum theory. We compare the theoretical results with single and multiple slits experiment data, and find the theoretical results are accordance with the experiment data. Otherwise, we give some theory prediction. We think all the new prediction will be tested by the light diffraction experiment.Comment: 10 page

A Fuzzy Rule for Improving the Performance of Multiobjective Job Dispatching in a Wafer Fabrication Factory

This paper proposes a fuzzy slack-diversifying fluctuation-smoothing rule to enhance the scheduling performance in a wafer fabrication factory. The proposed rule considers the uncertainty in the remaining cycle time and is aimed at simultaneous improvement of the average cycle time, cycle time standard deviation, the maximum lateness, and number of tardy jobs. Existing publications rarely discusse ways to optimize all of these at the same time. An important input to the proposed rule is the job remaining cycle time. To this end, this paper proposes a self-adjusted fuzzy back propagation network (SA-FBPN) approach to estimate the remaining cycle time of a job. In addition, a systematic procedure is also established, which can solve the problem of slack overlapping in a nonsubjective way and optimize the overall scheduling performance. The simulation study provides evidence that the proposed rule can improve the four performance measures simultaneously